docstr: Optimize the input model using pytorch built-in techniques.
  
  Args:
      model (torch.nn.Module): The pytorch model.
      model_params (ModelParams): The model parameters.
      input_tfms (MultiStageTransformation, optional): Transformations
          to be performed to the model's input tensors in order to
          get the prediction. Default: None.
      metric_drop_ths (float, optional): Threshold for the accepted drop
          in terms of precision. Any optimized model with a higher drop
          will be ignored. Default: None.
      quantization_type (QuantizationType, optional): The desired
          quantization algorithm to be used. Default: None.
      input_data (DataManager): User defined data. Default: None.
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code: def execute(
      self,
      model: Module,
      model_params: ModelParams,
      input_tfms: MultiStageTransformation = None,
      metric_drop_ths: float = None,
      quantization_type: QuantizationType = None,
      input_data: DataManager = None,
      **kwargs,
  ):
      """Optimize the input model using pytorch built-in techniques.
  
      Args:
          model (torch.nn.Module): The pytorch model.
          model_params (ModelParams): The model parameters.
          input_tfms (MultiStageTransformation, optional): Transformations
              to be performed to the model's input tensors in order to
              get the prediction. Default: None.
          metric_drop_ths (float, optional): Threshold for the accepted drop
              in terms of precision. Any optimized model with a higher drop
              will be ignored. Default: None.
          quantization_type (QuantizationType, optional): The desired
              quantization algorithm to be used. Default: None.
          input_data (DataManager): User defined data. Default: None.
      """
  
      if (
          quantization_type
          not in self.supported_ops[self.device.type.value]
      ):
          self.compiled_model = None
          return
  
      self.logger.info(
          f"Optimizing with {self.__class__.__name__} and "
          f"q_type: {quantization_type}."
      )
  
      self.compiled_model = self._compile_model(model, model_params)
  
file_path: nebuly/optimization/nebullvm/nebullvm/operations/optimizations/compilers/torch_dynamo.py
imports: from typing import Union, Any
  from nebullvm.core.models import ModelParams, QuantizationType
  from nebullvm.operations.optimizations.compilers.base import Compiler
  from nebullvm.optional_modules.torch import torch, Module, GraphModule
  from nebullvm.tools.data import DataManager
  from nebullvm.tools.transformations import MultiStageTransformation
label_desc: Use image node_id 1 for calling the TorchDynamoCompiler obj's underlying member method code with example usage: obj.execute(model, model_params, input_tfms, metric_drop_ths, quantization_type, input_data) without return types